Top Machine Learning Jobs in Toronto

The Director of Engineering, AI will lead AI initiatives, drive product strategy, manage engineering teams, and ensure effective technical execution in a fast-paced environment.

Lead technical initiatives in ML product development focusing on LLM and NLP systems. Mentor engineers, design models, and oversee data strategies while ensuring operational excellence and model performance.

As a Senior Research Engineer, you will optimize language and vision models on Cerebras hardware, focusing on high-performance ML inference techniques.

Lead Moneris' AI-driven product strategy, define roadmaps, collaborate with partners, evaluate technologies, and ensure scalable implementation across products.

Lead R&D in ML modeling for chatbots, develop AI-driven systems, design scalable models, and collaborate with cross-functional teams to enhance customer support.

In this role, you will integrate machine learning models into vehicle planning systems, improve decision-making algorithms, and ensure safety and reliability in real-world environments.

The Senior AI Engineer will develop AI-driven browser automation tools for navigating websites and extracting data, focusing on machine learning techniques and collaboration with data scientists for model optimization.

The Principal Machine Learning Engineer will define and execute the technology vision at EvenUp, leading advancements in generative AI, Document AI, and mentoring the ML organization to drive innovation and impact.

The Senior Staff Machine Learning Engineer will lead the design and scaling of advanced ML systems, mentor engineers, and define technical strategy across Affirm.

This internship focuses on AI research, specifically in natural language processing. Responsibilities include designing caching systems, developing retrieval mechanisms, and conducting experiments with NLP models.

Design and maintain infrastructure for scalable real-time data pipelines, develop tools for custom ML algorithms, and manage training and deployment for ML tasks.

As a Machine Learning Intern at Pinterest, you will lead ML projects, analyze data, build models, and collaborate with teams to solve complex problems.

As a Principal Software Engineer, you will design and optimize data-intensive platforms, lead engineering teams, mentor junior staff, and ensure system reliability in a fast-paced environment.

As a Staff Machine Learning Engineer, you will optimize Quora's ad products by applying machine learning algorithms, taking ownership of systems, and collaborating across teams.

The Junior Data Scientist will design and develop data models using Machine Learning and Analytics for trading and compliance in finance.

As an Enterprise Product Marketing Manager, you'll craft narratives for AI agents, develop executive content, and communicate value to enterprise clients.

Design and build production-grade cloud-native AI applications, fine-tune large language models, collaborate with cross-functional teams, and ensure software reliability and quality.

The Senior Technical Program Manager will oversee the development of Data and AI/ML platforms, manage enterprise-level initiatives, and ensure successful product delivery and technology adoption across the organization.

Develop and implement messaging and positioning for AI products, create marketing content, lead go-to-market strategies, and analyze market research.

As a Staff Machine Learning Engineer, you will design scalable AI infrastructure, implement AI systems, lead technical decisions, and collaborate with teams to enhance AI/ML solutions.

The Senior ML Platform Engineer will develop scalable machine learning solutions, manage ML infrastructure, and ensure observability in ML workflows while collaborating with cross-functional teams.

The role involves researching and implementing physics-informed AI/ML algorithms, designing workflows, mentoring, and collaborating with researchers to advance simulation and design tools for Autodesk.

Design and maintain ML pipelines and data pipelines, optimize fraud detection models, collaborate with teams, and ensure data quality for high-scale applications.

As a Staff Machine Learning Engineer, you will design and develop ML systems to combat fraud by building data pipelines, integrating data sources, and ensuring high-quality model performance while collaborating with teams.

Responsible for developing infrastructure for ML models, data pipelines, leading teams, ensuring compliance, and driving continuous improvement in data engineering practices.